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DTSTART;TZID=America/Los_Angeles:20260625T130000
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DTSTAMP:20260629T175334
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SUMMARY:BME/Genomics Seminar: Supervised and Unsupervised DeepGene Finding and Genome Foundation Models
DESCRIPTION:Presenter: Mario Stanke\, Professor of Bioinformatics\, University of Greifswald \nDescription: This talk will explore recent machine learning approaches for eukaryotic genome annotation. Our supervised ab initio deep gene finder\, Tiberius\, correctly predicts more than four times as many human protein-coding gene structures as its father\, Augustus\, and in some clades\, it approaches the accuracy of evidence-based pipelines such as BRAKER. Genome foundation models can automatically learn annotation-relevant embeddings from unannotated training genomes. I will also present Vipsania\, the unsupervised wife of Tiberius. Vipsania is a genome foundation model that learns hidden Markov models to find gene structures from naked genomes using a BERT-style masked language model objective. Finally\, I will report on ongoing efforts to use phylogenetic teaching signals from whole-genome vertebrate alignments to train a genome foundation model comparatively. \nKeywords: hidden Markov model layer\, linear recurrent unit\, continuous-time Markov chains on trees \nBio: Mario Stanke studied mathematics and computer science at the University of Göttingen and UCBerkeley\, and received his Dr. rer. nat. from the University of Göttingen. He completed a postdoctoral fellowship in the Haussler lab at UC Santa Cruz in 2006–2007. He has been a Professor of Bioinformatics at the Institute of Mathematics and Computer Science at the University of Greifswald since 2010. \nHosted by: Genomics Institute \nLocation: E2-599 (limited space) \nZoom: https://ucsc.zoom.us/j/95380317295?pwd=0HbwSYKRQqyCtBcPXGfoB0tPOsA16V.1
URL:https://events.ucsc.edu/event/bme-genomics-seminar-supervised-and-unsupervised-deepgene-finding-and-genome-foundation-models/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260625T140000
DTEND;TZID=America/Los_Angeles:20260625T160000
DTSTAMP:20260629T175335
CREATED:20260625T183144Z
LAST-MODIFIED:20260625T183144Z
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SUMMARY:Burbano\, L. (CS) - Security of autonomous decision-making agents: From control systems to embodied AI
DESCRIPTION:Due to their increasing complexity\, autonomous decision-making agents rely on increasingly advanced algorithms\, from classical control theory to reinforcement learning (RL) and\, more recently\, large vision-language models. While these algorithms help automate the decision-making in complex systems\, they bring newer attack vulnerabilities that an adversary can exploit. In this dissertation\, we study the security of autonomous decision agents that use control systems\, RL\, and AI. We focus on the security of cyber-physical and autonomous cyber-defense systems. In particular\, we study how an attacker can compromise decision-making agents. \nFor control systems\, this dissertation studies the existence of backdoor attacks against control systems that rely on data and proposes a defense strategy against the sensors of control systems. \nFor reinforcement learning\, we study the security of autonomous cyber-defense (ACD)) agents that automatically respond to attackers’ actions in a network. While previous works focus on creating agents\, we study an adversary who compromises the agent’s own infrastructure\, manipulating the information it observes to steer the network toward an attacker-chosen state. We also propose a defense strategy that focuses on determining if an attacker is compromising the ACD. \nFinally\, we study the security of embodied AI\, where CPS rely on large vision-language models (LVLMs) for decision-making. We propose a novel attack that can cause an agent to make unsafe decisions by presenting a well-designed textual sign via the visual modality. While previous attacks against neural network-based algorithms rely on creating adversarial patches without semantic meaning\, in this work\, we exploit the fact that LVLMs can understand text. \n  \nEvent Host: Luis Burbano\, Ph.D. Candidate\, Computer Science  \nAdvisor: Alvaro Cardenas \nZoom: https://ucsc.zoom.us/j/92373119649?pwd=BLFQMrGkOxJVXnjrJhXqudN1iciZAn.1 \nPasscode: 160434\n   
URL:https://events.ucsc.edu/event/burbano-l-cs-security-of-autonomous-decision-making-agents-from-control-systems-to-embodied-ai/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260625T180000
DTEND;TZID=America/Los_Angeles:20260625T190000
DTSTAMP:20260629T175335
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SUMMARY:UC Santa Cruz Premed Cohorts and DIY Info Session
DESCRIPTION:Join the UC Santa Cruz Premed Postbacc Program for an informative\, live online session designed for students\, parents\, and anyone exploring pathways into medicine\, public health\, and other health professions. \nIn this discussion\, we’ll provide a comprehensive overview of premed and pre-health programs to help you understand your options and choose the path that best aligns with your goals. \nThis is a presentation and discussion with plenty of time to ask questions and connect directly with program staff. \nClaim your seat today. 
URL:https://events.ucsc.edu/event/uc-santa-cruz-premed-cohorts-and-diy-info-session/
LOCATION:Silicon Valley Campus\, 3175 Bowers Avenue\, Santa Clara\, CA\, 95054\, United States
CATEGORIES:Lectures & Presentations,Meetings & Conferences,Training
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